ES2770730T3 - Location within an environment using sensor fusion - Google Patents

Abstract

An apparatus comprising: a plurality of data systems (128) configured to generate a plurality of data streams (126) in which the plurality of data streams includes a first type of data stream (132) and a second type data stream (130), wherein each data point in the first type of data stream includes an uncertainty measurement, and wherein each data point in the second type of data stream does not include an uncertainty measurement; a modifier (120) configured to apply a probability distribution (137) to the second type of data stream to form a modified data stream (135); and a pose estimator (118) located aboard a mobile platform (104) and configured to receive and merge the first type of data stream and the modified data stream to generate a pose estimate (122) with a desired level precision for the mobile platform with respect to an environment (100) around the mobile platform; wherein the apparatus is configured to: observe a current landmark in the environment a selected number of times using an on-board data system (134), while the mobile platform is at a current location; identifying an initial relative distance between the current location of the mobile platform and the current reference point; and moving the mobile platform from the current location to a new location as far as possible to a desired location without losing the current reference point within a field of view of an onboard data system sensor system.

Description

DESCRIPTION

Localization within an environment using sensor fusion

Background information

1. Field:

This disclosure generally relates to identifying the pose of a mobile platform in an environment. More particularly, the present disclosure relates to a method and apparatus for forming and merging data streams that each include a measure of uncertainty to generate a pose estimate for the mobile platform within the environment.

2. Background:

In some situations, it may be desirable to have a mobile robot that can move freely within an environment in the same way that a human would. Physical landmarks, such as paint, tape, or magnets, which can typically be used to help a mobile robot move within an environment, can force a mobile robot to follow only predefined routes. In addition, the installation of these types of physical landmarks can be slower and more expensive than desired. To move more freely within an environment, a mobile robot may need to perform localization, which includes identifying the pose of the mobile robot within the environment. As used herein, a "pose" includes a position, an orientation, or both with respect to a reference coordinate system.

A mobile robot can use an external sensor system to perform location. However, in some cases, the line of sight between a mobile robot and the external sensor system can be obstructed by other objects, robots, and / or people within the manufacturing environment. As an example, in an aircraft manufacturing environment, line of sight can be lost when the mobile robot operates under an aircraft wing, inside the wing, near factory objects such as cranes or columns, and / or in restricted areas. Once line of sight is lost, the mobile robot may stop receiving pose updates and may need to stop operations until line of sight has recovered. Without location, the mobile robot may be unable to navigate through the environment as precisely as it wants.

Additionally, in a dynamic environment, cars, aircraft, workstations, vehicles, equipment platforms, other types of devices, human operators, or some combination thereof can move. Consequently, a mobile robot may be unable to rely solely on its environment to move through this type of environment or a cluttered or non-segmented or structured environment efficiently. Currently available mobile robots may be unable to operate at desired levels of performance and efficiency or maneuver around human operators as safely as desired in these different types of environments.

Also, in some cases, the equipment or devices used for locating may be more expensive, larger, or heavier than desired. In certain situations, the processing required to perform the location to a desired level of precision may take longer or require more processing resources than desired. Therefore, it would be desirable to have a method and apparatus that takes into account at least some of the problems discussed above, as well as other possible problems.

The United States patent application publication US 2009/0024251 A1, according to its abstract, states: a method and apparatus for estimating the pose of a mobile robot using a particle filter. The apparatus includes an odometer which detects a variation of the pose of a mobile robot, a feature processing module which extracts at least one feature from an upward image captured by the mobile robot, and a filter module of Particles which determines the current poses and weights of a plurality of particles by applying the movable robot pose variation detected by the odometer and the feature extracted by the feature processing module to the previous poses and weights of the particles.

The United States patent application publication US 2004/0167667 A1, according to its abstract, states: methods and apparatus using a visual sensor and dead calculus sensors to process Simultaneous Location and Mapping (SLAM). These techniques can be used in robot navigation. These visual techniques can be used to autonomously generate and update a map. Unlike laser rangefinders, visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as the environments in which people move. An example uses multiple particles to maintain multiple hypotheses regarding location and mapping. Furthermore, one embodiment maintains the particles in a relatively computational efficient manner, thus allowing SLAM processes to be performed in software using relatively inexpensive microprocessor-based computer systems.

United States patent application publication US 2005/0182518 A1, according to its abstract, states: methods and apparatus that allow measurements from a plurality of sensors to be robustly combined or fused. For example, the sensors may correspond to sensors used by a mobile device, such as like a robot, for localization and / or mapping. Measurements can be merged to estimate a measurement, such as a pose estimate for a robot.

European Patent Application EP 0570581 A1, according to its abstract, establishes: a sensory system for vehicle navigation that employs a sensor, such as a compass, or an odometer, to detect a navigation parameter of a vehicle. A signal is provided from the sensor, which has a wrong component. A free recognition pattern, such as a fuzzy inference browser computer, is employed to recognize this erroneous behavior. After recognition, the sensor can be recalibrated or assigned a clarity coefficient. The signal is then combined with other measurements of the same parameter and mapped before deriving the final navigation variable.

The United States patent application publication US 2007/0156286 A1, according to its abstract, states: a mobile robot equipped with a rangefinder and a stereo vision system. The mobile robot is capable of autonomously navigating through urban terrain, generating a map based on rangefinder data, and transmitting the map to the operator, as part of various operator-selectable reconnaissance operations. The mobile robot employs a Hough transformation technique to identify linear features in its surroundings and then aligns itself with the identified linear features in order to navigate urban terrain; At the same time, a scaled vector field histogram technique is applied to the combination of the rangefinder and stereo vision data to detect and prevent obstacles that the mobile robot encounters when navigating autonomously. In addition, the missions carried out by the mobile robot can include limitation parameters based on distance or elapsed time, to guarantee the completion of autonomous operations.

Summary

In the present application, an apparatus is described comprising: a plurality of data systems configured to generate a plurality of data streams in which the plurality of data streams include a first type of data stream and a second type of stream data, wherein each data point in the first type of data stream includes an uncertainty measurement, and wherein each data point in the second type of data stream does not include an uncertainty measurement; a modifier configured to apply a probability distribution to the second type of data stream to form a modified data stream; and a pose estimator located aboard a mobile platform and configured to receive and merge the first type of data stream and the modified data stream to generate a pose estimate with a desired level of precision for the mobile platform with respect to an environment around the mobile platform; wherein the apparatus is configured to: observe a current landmark in the environment a selected number of times using an on-board data system, while the mobile platform is at a current location; identifying an initial relative distance between the current location of the mobile platform and the current reference point; and moving the mobile platform from the current location to a new location as far as possible to a desired location without losing the current reference point within a field of view of an onboard data system sensor system.

Also described is a method for guiding a mobile platform within an environment, the method comprises: generating a first type of data stream and a second type of data stream using a plurality of data systems, wherein each data point in the first type of data stream includes an uncertainty measurement, and wherein each data point in the second type of data stream does not include an uncertainty measurement; applying a probability distribution to the second type of data stream to form a modified data stream; merging the first type of data stream and the modified data stream to generate a pose estimate with a desired level of precision for the mobile platform relative to the environment around the mobile platform; and observing a current landmark in the environment a selected number of times using an on-board data system, while the mobile platform is at a current location; identifying an initial relative distance between the current location of the mobile platform and the current reference point; and moving the mobile platform from the current location to a new location as far as possible to a desired location without losing the current reference point within a field of view of an onboard data system sensor system.

Also described is a mobile platform that may comprise a base, a controller associated with the base and a movement system associated with the base. The controller may further be configured to receive data streams from a plurality of data systems in which the data streams may include a number of the first type of data stream and a number of the second type of data stream. The controller may comprise a modifier and a pose estimator. The modifier can be configured to apply a probability distribution to each of the number of the second type of data streams to form a number of modified data streams. The pose estimator can be configured to receive the number of the first type of data streams and the number of modified data streams. The pose estimator may further be configured to merge the plurality of data streams together to generate a pose estimate with a desired level precision for the mobile platform relative to an environment around the mobile platform. The motion system can be configured to be controlled by the controller based on the pose estimate to move the mobile platform within the environment.

The features and functions can be independently achieved in various embodiments of the present disclosure or they can be combined in still other embodiments in which additional details can be seen with reference to the following description and drawings.

Brief description of the drawings

Novel features believed to be characteristic of illustrative embodiments are set forth in the appended claims. However, the illustrative embodiments, as well as a preferred mode of use, additional objectives, and features thereof, will be better understood with reference to the following detailed description of an illustrative embodiment of the present disclosure when read in conjunction with the accompanying drawings. , wherein: Figure 1 is an illustration of an environment in block diagram form in accordance with an illustrative embodiment;

Figure 2 is an illustration of a plurality of data systems in block diagram form in accordance with an illustrative embodiment;

Figure 3 is an illustration of the components of a plurality of data systems that are located onboard and the components of a plurality of data systems that are located outboard in accordance with an illustrative embodiment;

Figure 4 is an illustration of a manufacturing environment in accordance with an illustrative embodiment;

Figure 5 is an illustration of a mobile robot in accordance with an illustrative embodiment;

Figure 6 is an illustration of a process for generating a pose estimate for a mobile platform in an environment in the form of a flow chart in accordance with an illustrative embodiment;

Figure 7 is an illustration of a process for guiding a mobile robot within a manufacturing environment in the form of a flow chart in accordance with an illustrative embodiment;

Figure 8 is an illustration of a data processing system in the form of a block diagram in accordance with an illustrative embodiment;

Figure 9 is an illustration of an aircraft manufacturing and servicing method in the form of a block diagram in accordance with an illustrative embodiment; and

Figure 10 is an illustration of an aircraft in the form of a block diagram in accordance with an illustrative embodiment.

Detailed description

The illustrative embodiments recognize and take into account different considerations. For example, the illustrative embodiments recognize and take into account that it may be desirable to have a method and apparatus capable of performing more accurate and rapid location for a number of mobile platforms within a manufacturing environment. Furthermore, the illustrative embodiments recognize and take into account that it may be desirable to have a method and apparatus for generating a pose estimate for a mobile robot within an environment aboard the mobile robot. Therefore, the illustrative embodiments provide a method and apparatus for generating a pose estimate for a mobile robot on board the mobile robot using sensor fusion. The method and apparatus provided by the illustrative embodiments can reduce the time required to generate a pose estimate, while increasing the accuracy of the estimate. Furthermore, the solution provided by the illustrative embodiments may be simpler and more cost effective than some currently available solutions.

Referring now to the figures, and in particular with reference to Figure 1, an illustration of an environment is depicted in accordance with an illustrative embodiment. In this illustrative example, environment 100 can be any environment in which the number of mobile platforms 102 can be used. As used in this document, a "number of" items can be one or more items. Thus, the number of mobile platforms 102 can include one or more mobile platforms.

In an illustrative example, environment 100 can take the form of manufacturing environment 101 in which object 103 is manufactured. Object 103 can take a number of different forms. For example, without limitation, object 103 may take the form of a door, a skin panel, a wing for an aircraft, a fuselage for an aircraft, a structural component for a building, a set of components, or some other type of device. object.

As shown, mobile platform 104 may be an example of an implementation for a mobile platform in the number of mobile platforms 102. In this illustrative example, the mobile platform 104 may take the form of the mobile robot 106. Of course, depending on the implementation, the mobile platform 104 can take the form of any type of platform, structure, device or object capable of at least partially autonomously moving within environment 100.

As shown, mobile robot 106 may include base 108, motion system 110, number of tools 112, and controller 114. Motion system 110, number of tools 112, and controller 114 may be associated with base 108. As used herein, when one component is "associated" with another component, the association is a physical association in the depicted examples.

For example, without limitation, a first component, such as motion system 110, can be considered associated with a second component, such as base 108, by being secured to the second component, attached to the second component, mounted to the second component, welded. to the second component, attached to the second component and / or connected to the second component in some other suitable manner. The first component can also be connected to the second component using a third component. Furthermore, the first component can be considered to be associated with the second component by being formed as a part and / or as an extension of the second component.

Motion system 110 can be used to move mobile robot 106 within environment 100. For example, without limitation, motion system 110 can be used to move mobile robot 106 within environment 101. Depending on the implementation, system 110 The motion device may include at least one of a number of wheels, a number of rollers, a number of legs, a number of holonomic wheels, or other types of devices capable of providing motion.

As used in this document, the phrase "at least one of", when used with a list of items, means that different combinations of one or more of the listed items may be used and only one of the items may be required in the list. The item can be a particular object, thing, or category. In other words, "at least one of" means that any combination of items or number of items in the list can be used, but not all items in the list may be required.

For example, "at least one of item A, item B, and item C" can mean item A; element A and element B; element B; element A, element B and element C; or element B and element C. In some cases, "at least one of element A, element B and element C" can mean, for example, without limitation, two of element A, one of element B and ten of element C; four from element B and seven from element C; or some other suitable combination.

In this illustrative example, a number of tools 112 may be used to perform a number of operations 116 within environment 100. At least one of the number of operations 116 may be performed on object 103. The number of operations 116 may include, for example For example, without limitation, at least one of a drilling operation, a fixing operation, a sanding operation, a painting operation, a machining operation, a test operation, an image capture operation, or some other type of operation. Thus, the number of tools 112 may include, for example, without limitation, at least one of a drilling device, a clamping device, a sanding tool, a painting tool, a fluid dispensing system, a device. sealant application, a machining device, a milling device, a testing system, an image capture device, a scanner, a marker, a pen, a label applicator, or some other type of tool.

In this illustrative example, controller 114 can be configured to control the operation of at least one of the number of tools 112. Additionally, controller 114 can be configured to control motion system 110. In particular, controller 114 may control motion system 110 to move mobile robot 106 along path 117 in environment 100. Path 117 may be at least partially along floor 115 of environment 100. As used herein, floor 115 may include a floor surface, a surface on a bridge, a surface made up of one or more pallets, a platform surface, a floor of an elevator, a floor of a conveyor belt, some another type of surface, or some combination thereof.

Route 117 can be a dynamically updated route in which controller 114 can update route 117 as mobile robot 106 moves through environment 100. Controller 114 can update route 117 as mobile robot 106 becomes moves through environment 100 to assist mobile robot 106, for example, without limitation, at least one of preventing obstacles, moving around objects that have recently been placed or moved within environment 100, responding to changes in the number of operations 116 to be performed by mobile robot 106, maneuver around human operators who are in or moving within environment 100, or respond to some other type of new or changed circumstance within environment 100. Controller 114 may use location to help the mobile robot 106 navigate.

As shown, controller 114 can include, for example, without limitation, pose estimator 118 and modifier 120. Pose estimator 118 can generate pose estimate 122 for mobile robot 106. The pose estimate 122 may be a pose estimate of the mobile robot 106 within environment 100. The pose of the mobile robot 106, as used herein, may comprise at least a position of the mobile robot 106 or an orientation of the robot 106 mobile relative to the reference coordinate system 124 for the environment 100. Therefore, the pose estimate 122 may be composed of at least one of the position estimate 121 and the orientation estimate 123 of the mobile robot 106 relative to the reference coordinate system 124 for environment 100.

The mobile robot 106 can be configured to move with six degrees of freedom in the environment 100. Therefore, the pose estimate 122 can be a six-degree-of-freedom (6DoF) pose estimate for the mobile robot 106. In some cases, the pose estimate 122 may refer to the pose estimate of the mobile robot 106 in the space of six degrees of freedom of the mobile robot 106.

In this illustrative example, the pose estimator 118 may merge a plurality of data streams 126 to generate the pose estimate 122. At least a portion of the plurality of data streams 126 may be received from a plurality of data systems 128. As used herein, a data system in a plurality of data systems 128 may include a number of sensor systems, a number of processor units, or some combination thereof. As used herein, a "sensor system" can comprise any number of sensor devices, active devices, passive devices, or a combination thereof.

As used herein, "merging" the plurality of data streams 126 may mean combining and processing the different data streams in the plurality of data streams 126 to generate a single pose estimate 122. Each of the plurality of data streams 126 may be composed of estimates generated over time.

Data stream 125 is an example of a plurality of data streams 126. Data stream 125 may be generated by one of the plurality of data systems 128. In this illustrative example, the data stream 125 may be comprised of estimates generated over time.

As used herein, an "estimate" can be a six-degree-of-freedom pose estimate of the mobile robot 106. This estimate can be generated based on measurements generated at a single point in time or over a period of time. In some cases, the estimate may also include metadata. In some illustrative examples, the estimate may be referred to as an output data point such that the data stream 125 may be composed of a plurality of output data points.

The pose estimator 118 may use a plurality of data streams 126 and a Bayesian estimation algorithm to generate the pose estimate 122. In particular, the pose estimator 118 can use the following Bayesian estimation equations:

p {X t + í | ZT) = f [p ( X t + 1 | XT) * p ( X t | ZT) j (1)

where t is time, xt + i is the pose of the mobile robot 106 at time t + 1, zt is the collection of all the estimates in the plurality of data streams 126 over time, T, p ( xt + i \ ZT) is the probability of xt + i given zt, xt is the pose of the mobile robot 106 at time t, n is the total number of data systems in the plurality of data systems 128, Z ^ - is the estimate generated by a first data system in the plurality of data systems 128 at time t + 1, and Z ^ j- is the estimate generated by the nth data system in a plurality of data systems 128 at time t +1. For any given time t + 1, p ( Z - ^, ..., Z ^ | X £ + 1) only includes data systems that have provided estimates at time t + i .

The error in the pose estimator 118 can be further reduced by increasing the number of data streams in the plurality of data streams 126 used to generate the pose estimate 122, and thus the number of data sets in the plurality of 128 data systems. In other words, as the number of data streams in the plurality of data streams 126 increases, the error in pose estimate 122 generated by pose estimator 118 decreases.

The use of Bayesian estimation techniques to generate the pose estimate 122 may require that all data used to generate the pose estimate 122 be probabilistic. In other words, all data may need to include randomness or uncertainty.

However, the data streams 127 generated by the plurality of data systems 128 may include the number of the first type of data streams 132 and the number of the second type of data streams 130. A "first type of data stream", such as one of the number of the first type of data streams 132 may include data points in which each data point includes, or is coupled with, an uncertainty measurement, based on some probability distribution. In particular, a first type of data flow can be generated by a probabilistic system, pattern or algorithm in which the output or the way an output is given for a given input takes into account randomness or a degree of uncertainty. Thus, each of the first types of data streams 132 may be referred to as a probabilistic data stream in some illustrative examples.

As used herein, a "second type of data stream", such as one of the number of the second type of data streams 130, may include data points in which each data point does not include, or is not coupled with , a measurement of uncertainty. For example, without limitation, the data point can include only one data value only. In some illustrative examples, this second type of data flow may be referred to as a pseudo-deterministic data flow.

The number of the second type of data streams 130 in data streams 127 can be received by modifier 120 in pose estimator 118. Modifier 120 can be configured to modify a second type of data stream so that pose estimator 118 can use the data stream. In particular, the modifier 120 can convert the number of the second type of data streams 130 into a number of the first type of processed data streams. All of the first type of data streams can be processed by the pose estimator 118 to generate the pose estimate 122.

As an illustrative example, the second type of data stream 133 may be generated by one of a plurality of data systems 128. In this example, the second type of data stream 133 may have been generated using one or more odometry techniques. Modifier 120 may be configured to modify the second type of data stream 133 to form modified data stream 135 that can be used by pose estimator 118. Modifier 120 can convert the second type of data stream 133 into modified data stream 135 using any number of techniques. As an illustrative example, modifier 120 may apply probability distribution 137 to the second type of data stream 133 to form modified data stream 135.

Depending on the implementation, the probability distribution 137 can be a Gaussian distribution or some other type of probability distribution. The probability distribution 137 may be a predetermined probability distribution. For example, without limitation, the probability distribution 137 may have been determined empirically, using a mathematical pattern, or in some other way before the mobile robot 106 was used to perform a number of operations 116 in environment 100.

By converting the second type of data stream 133 to the modified data stream 135, the need for probabilistic data generated using a pattern based on the physics of the mobile robot 106 can be eliminated. In particular, the term p ( x + i | xt) in equation (1) described above can be provided using at least one of the data streams 127 of the plurality of data sets 128 rather than a pattern based on the physical. For example, without limitation, the pose estimator 118 may use a data stream from an odometry system in a plurality of data systems 128 to provide the term p ( xt + i | xt) in equation (1) described above. . The term p ( xt + i | xt) is the probability of the pose of the mobile robot 106 at time t + 1 given the pose of the mobile robot 106 at time t.

In this way, a probability distribution can be applied to each number of the second type of data streams 130 by the modifier 120 to form the number of modified data streams 129. Each of the number of modified data streams 129 may be equal to each of the number of the first type of data streams 132. The number of modified data streams 129 and the number of the first type of data streams 132 together can form a plurality of data streams 126 used by the pose estimator 118 to form a pose estimate 122.

In these illustrative examples, the plurality of data systems 128 may include the number of onboard data systems 134 and the number of outboard data systems 136. As used herein, an "on-board data system", such as one of the number of on-board data systems 134, can be configured to generate a data stream on board the mobile robot 106. In some cases, an on-board data system may be completely separate from the controller 114. In other illustrative examples, at least a portion of an on-board data system may be implemented or integrated with the controller 114. The data stream generated by the Onboard data system may be received by pose estimator 118 or modifier 120, depending on whether the data stream is a second type of data stream or a first type of data stream.

The onboard data system 144 may be an example of one of the number of onboard data systems 134. The on-board data system 144 can include at least one of a passive element, an active element, a processor unit, an integrated circuit, a microprocessor, a sensor system, a target, or some type of other device or element. At least the portion of the onboard data system 144 that generates a data stream is on board the mobile robot 106. In this manner, the entire onboard data system 144 may be located on board the mobile robot 106 or a portion of the onboard data system 144 may be located on board, while another portion may be located off board.

As used herein, an "outboard data system", such as one of number of outboard data systems 136, may be a data system configured to generate a data stream remotely from the mobile robot 106. The data stream generated by the outboard data system can be sent to controller 114 using, for example, without limitation, a wireless communications link.

The outboard data system 145 may be an example of one of the number of outboard data systems 136. The outboard data system 145 may include at least one of a passive element, an active element, a processor unit, an integrated circuit, a microprocessor, a sensor system, a target, or some other type of device or element. At least the portion of the outboard data system 145 that generates a data stream is located in the mobile outboard robot 106. In this manner, the entire outboard data system 145 may be located outboard or a portion of the outboard data system 145 may be located outboard, while another portion may be located onboard.

In addition, controller 114 can be configured to reduce error in moving mobile robot 106 along path 117. In particular, controller 114 can reduce random error in moving mobile robot 106 from starting location 138 to along route 117 to the desired location 140 along route 117 within selected tolerances. In an illustrative example, controller 114 may use one or more of the number of on-board data systems 134 to reduce this random error.

In particular, controller 114 may use one or more of the number of on-board data systems 134 configured to observe the number of reference points 142 within environment 100 to reduce random error when moving mobile robot 106 from location 138 along route 117 to the desired location 140 along route 117. A landmark in the number of waypoints 142 can be any recognizable feature in environment 100. For example, without limitation, A landmark can take the form of a pillar, a platform, a structural feature, a piece of equipment, an artificial structure, a target, a tag, or some other type of landmark.

In this illustrative example, the on-board data system 144 may include a sensor system capable of observing at least one of the number of reference points 142 in the environment 100 while the mobile robot 106 is at the initial location 138 within of the environment 100. For example, without limitation, the on-board data system 144 may observe the reference point 146 of the number of reference points 142 at the same time that it is at the initial location 138. The observation of the reference point 146 can be done a selected number of times. For example, N observations can be made of the reference point 146.

The landmark 146 can be a natural or artificial landmark, depending on the implementation. In this illustrative example, reference point 146 may be a stationary reference point. However, in another illustrative example, reference point 146 may be mobile and capable of moving within environment 100 as necessary. In some illustrative examples, the reference point 146 may be a person.

The on-board data system 144 can be used to identify an initial relative distance between the home location 138 of the mobile robot 106 and the reference point 146. As the number of observations of the reference point 146 increases, the error in the initial relative distance between the initial location of the mobile robot 106 and the reference point 146 decreases. The reduction in error is based on the central limit theorem.

In particular, the central limit theorem can be exploited in such a way that the error can be reduced by a factor of the square root of n, where n is the total number of observations made. The central limit theorem states that, under certain conditions, the sum of n identically distributed independent random variables, when properly scaled, can converge in the distribution to a standard normal distribution. Therefore, in an illustrative example, as n increases, the empirical covariance will decrease at a given rate as follows:

(<r) / V ñ (3)

where a is the standard deviation from the mean.

The mobile robot 106 can then be moved to a new location as far as possible towards the direction of the desired location 140 without losing the reference point 146 within the field of view of the sensor system of the onboard data system 144. The on-board data system 144 can identify a new relative distance between the new location of the mobile robot 106 and the reference point 146. The difference between the initial relative distance and the new relative distance can then be calculated with minimal error and used to determine the new location of the mobile robot 106.

If the new location is not the desired location 140 within the selected tolerances, the mobile robot 106 can then be moved closer to the desired location 140 using the new reference point 147. In particular, the onboard data system 144 can search the new reference point 147 in the number of reference points 142, while at the new location. While at the new location, the onboard data system 144 can then observe the new reference point 147 the number, N, of times selected. In this way, the new reference point 147 can be considered "correlated" with the reference point 146 at the new location.

The process of moving to another location as close as possible to the desired location 140 and operations performed while at this other location can be repeated, as described above. This type of movement and processing can be repeated until the mobile robot 106 has reached the desired location 140 within the selected tolerances. This type of process can reduce the overall error associated with moving the mobile robot 106 from the initial location 138 to the desired location 140 within selected tolerances, compared to moving the mobile robot 106 without using the number of points 142 of reference and observe each reference point from the number of reference points 142 a number, N, of times selected.

Referring now to Figure 2, an illustration of the plurality of data systems 128 of Figure 1 is depicted in the form of a block diagram in accordance with an illustrative embodiment. As represented, the plurality of data systems 128 may include the number of onboard data systems 134 and the number of outboard data systems 136.

In this illustrative example, the plurality of data systems 128 may include the inertial measurement unit 202, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, the light sensing and measurement, indoor global positioning system 212, motion capture system 214, and laser system 216. The inertial measurement unit 202, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, and the light detection and measurement system 210 can be part of the number of systems 134 of data on board. The inboard global positioning system 212, motion capture system 214, and laser system 216 may be part of the number of outboard data systems 136.

In this illustrative example, the inertial measurement unit 202 can measure the relative displacement of the mobile robot 106 within the environment 100 by sensing speed, orientation, and acceleration. The inertial measurement unit 202 may generate the data stream 203 which can be sent to the controller 114 as one of the data streams 127. Depending on the way in which the inertial measurement unit 202 is implemented, the data stream 203 may be considered one of the number of the first type of data streams 132 or one of the number of the second type of data streams 130.

The color and depth odometry system 204 can be used to provide color data and depth data for the environment 100. The wheel odometry system 206 can be used to measure the relative displacement of the mobile robot 106 within the environment 100 when the system 110 of motion in Figure 1 includes wheels. Visual odometry system 208 may use cameras to estimate the relative displacement of mobile robot 106 within environment 100. Light sensing and measurement system 210 may generate laser scans of environment 100.

Each of the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, and the light detection and measurement system 210 can be fully located on board the mobile robot 106. In an illustrative example, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, and the light detection and measurement system 210 can generate data stream 205, stream 207 data, data stream 209 and data stream 211, respectively, which may be sent to controller 114 as part of data streams 127. In this illustrative example, each of data stream 205, data stream 207, data stream 209, and data stream 211 can be included in the number of the first type of data stream 132 or the number of the second type of data streams 130, depending on implementation. In this illustrative example, each of data stream 205, data stream 207, data stream 209, and data stream 211 can be included in the number of the second type of data streams 130.

In other illustrative examples, one or more of data stream 205, data stream 207, data stream 209, and data stream 211 generated by color and depth odometry system 204, wheel odometry system 206 , the visual odometry system 208 and the light detection and measurement system 210, respectively, can be sent to the locator and mapper 218. The locator and mapper 218 can be implemented within the controller 114 in Figure 1 or separately from the controller 114, depending on the implementation.

In addition, the locator and mapper 218 may take the form of the two-dimensional locator and mapper 220 or the three-dimensional locator and mapper 222, depending on the implementation. In some cases, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, the light detection and measurement system 210, and the locator and mapper 218 may together form the system. 224 location and mapping. The location and mapping system 224 can be considered an on-board data system in the number of on-board data systems 134.

The locator and mapper 218 can be configured to simultaneously estimate a metric map of the environment 100 and an estimate of a pose of the mobile robot 106 within this metric map based on all data streams received at the locator and the mapper 218. The map Metric can be two-dimensional or three-dimensional, depending on the implementation. In an illustrative example, locator and mapper 218 may be referred to as a simultaneous location and mapping (SLAM) system. In these examples, the environment 100 metric map estimate and the mobile robot 106 pose estimate within this metric map can be sent in the form of data stream 213 to controller 114 in Figure 1 as one of the stream 127 of data. The data stream 213 may be one of the number of the first type of data streams 132 or one of the number of the second type of data streams 130.

In this illustrative example, the indoor global positioning system 212 includes the number of sensor devices 226, the number of transmitters 228, and the server 230. The number of transmitters 228 can be located outboard, as well as the number of devices Sensor 226 can be located on board mobile robot 106.

The number of transmitters 228 can be configured to generate the number of light signals 229. The number of light signals 229 may include at least one of a laser signal, an infrared signal, or some other type of light signal. The number of sensor devices 226 can be passive and used to detect the number of light signals 229 transmitted from the number of transmitters 228. The number of sensor devices 226 can send data 231 about the detected number of light signals 229 to the server 230 .

Server 230 can be configured to use this data to estimate the pose of mobile robot 106 within environment 100 over time. Estimates generated over time can form data stream 233 that can be sent to controller 114 as one of data streams 127. The data stream 233 may be one of the number of the first type of data streams 132 or one of the number of the second type of data streams 130, depending on the implementation. Server 230 may be located outboard. In this manner, the server 230 can be an outboard data source which makes the inboard global positioning system 212 one of the number of outboard data systems 136.

The motion capture system 214 may include the motion capture target 232, the image capture system 234, and the motion capture server 236. The motion capture target 232 may be passive and located aboard the mobile robot 106. Image capture system 234 can be located outboard within environment 100 in Figure 1 and used to generate motion capture data 235 for and track motion capture target 232. The motion capture data 235 generated by the motion capture system 214 can be sent to the motion capture server 236 for further processing.

Motion capture server 236 may then send motion capture data 235 in the form of data stream 237 to controller 114 as one of data streams 127. In some cases, the motion capture server 236 may process motion capture data 235 to form the data stream 237. The data stream 237 may be one of the number of the first type of data streams 132 or one of the number of the second type of data streams 130, depending on the implementation. The motion capture server 236 can be located outboard within the environment 100. In this way, the motion capture server 236 can be considered an outboard data source, which makes the motion capture system 214 one of number of outboard data systems 136.

As shown, laser system 216 may include laser target 238 and laser sensor 240. Laser target 238 can be passive and located on board mobile robot 106. Laser sensor 240 can be located outboard within environment 100 and used to track the movement of laser target 238. The laser sensor 240 can measure the position of the laser target 238 and process this data to generate an estimate of a pose of the mobile robot 106, which can form the data stream 241 over time. The data stream 241 may be one of the number of the first type of data streams 132 or one of the number of the second type of data streams 130, depending on the implementation. Laser sensor 240 may send data stream 241 to controller 114 as one of data streams 127.

In this manner, various types of sensor systems and devices can be used to generate streams 127 of data. The number of the second type of data streams 130 in the data streams 127 may be processed by the modifier 120 in Figure 1 to form the number of modified data streams 129. Together, the number of modified data streams 129 in Figure 1 and the number of the first type of data streams 132 can form a plurality of data streams 126 in Figure 1 used by pose estimator 118 to generate an estimate 122 pose.

Referring now to Figure 3, an illustration is depicted of the components of the plurality of onboard data systems 128 and the components of the plurality of data systems 128 that are outboard as described in the Figure 2 according to an illustrative embodiment. As shown, some of the components of the plurality of data systems 128 of Figure 2 are located onboard 300, while other components of the plurality of data systems 128 of Figure 2 are located offboard 302. . In Figure 3, onboard 300 means aboard mobile robot 106 in Figure 1 and outboard 302 means outboard with respect to mobile robot 106 in Figure 1.

In particular, the inertial measurement unit 202, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, the light detection and measurement system 210, and the locator and mapper. 218 of Figure 2 are on board 300. In addition, the number of sensor devices 226, the motion capture target 232 and the laser target 238 of Figure 2 are also on board 300. The number of transmitters 228, server 230, image capture system 234, motion capture server 236, and laser sensor 240 can be located outboard.

In an illustrative example, the color and depth odometry system 204, the wheel odometry system 206, the visual odometry system 208, and the light detection and measurement system 210 send data stream 205, data stream 207 data, data stream 209 and data stream 211, respectively, to locator and mapper 218. Locator and mapper 218 can then use these data streams to form data stream 213 and send data stream 213 to the controller 114 of Figure 1, which is also on board 300. Inertial measurement unit 202 can send data stream 203 directly to controller 114. In this illustrative example, these data streams can be sent to controller 114 using any number of wired or wireless communications links.

Additionally, server 230, motion capture server 236, and laser sensor 240 can send data stream 233, data stream 237, and data stream 241 to controller 114. In this illustrative example, these data streams they can be sent to controller 114 wirelessly.

A data stream that is sent to controller 114 may be received by pose estimator 118 if the data stream is a first type of data stream in the number of the first type of data streams 132 in Figure 1 or the modifier 120 if the data stream is a second type of data stream in the number of the second type of data stream 130 in Figure 1.

The illustrations of the environment 100 in Figure 1, the plurality of data systems 128 in Figure 2, and the components located onboard 300 and offboard 302 in Figure 3 do not imply physical or architectural limitations on the manner in which you can implement an illustrative embodiment. Other components may be used in addition to or in place of those illustrated. Some components may be optional. In addition, the blocks are presented to illustrate some functional components. One or more of these blocks can be combined, divided or combined and divided into different blocks when implemented in an illustrative embodiment.

Referring now to Figure 4, an illustration of a manufacturing environment in accordance with an illustrative embodiment is depicted. In this illustrative example, the manufacturing environment 400 may be an example of an implementation for the manufacturing environment 101 in Figure 1. As shown, the aircraft wing 402 can be manufactured within the manufacturing environment 400. Aircraft wing 402 may be an example of an implementation for object 103 in Figure 1.

Mobile robots 404 can be used to perform the operations necessary to fabricate the aircraft wing 402. The mobile robots 404 can be an example of an implementation for the number of mobile platforms 102 in Figure 1. In this illustrative example, the mobile robots 404 can be configured to move on the floor 406 of the manufacturing environment 400. Each of the mobile robots 404 may be able to identify its position within and navigate through the manufacturing environment 400.

Referring now to Figure 5, an illustration of a mobile robot in accordance with an illustrative embodiment is depicted. In this illustrative example, mobile robot 500 may be an example of an implementation for mobile robot 106 in Figure 1. Additionally, mobile robot 500 may be an example of a way in which each of the robots 404 can be implemented. mobiles in Figure 4.

As shown, the mobile robot 500 may include the base 502, the motion system 504, and a plurality of devices 506. In this illustrative example, the plurality of devices 506 may include a light sensing and measuring system 508, a 510 for color and depth odometry, and targets 512. The light detection and measurement system 508 may be an example of an implementation for the light detection and measurement system 210 in Figure 2. The color odometry system 510 and depth can be an example of an implementation for the color and depth odometry system 204 in Figure 2 Targets 512 can be an example of an implementation for the motion capture target 232 and laser target 238 in Figure 2 .

The illustrations of Figures 4 to 5 are not intended to imply physical or architectural limitations on the manner in which an illustrative embodiment may be implemented. Other components may be used in addition to or in place of those illustrated. Some components may be optional.

The different components shown in Figures 4-5 may be illustrative examples of how the components shown in block form in Figures 1-3 can be implemented as physical structures. Also, some of the components in Figures 4-5 can be combined with components in Figures 1-3, used with components in Figures 1-3, or a combination of the two.

Referring now to Figure 6, an illustration of a process for generating a pose estimate for a mobile platform in an environment is shown in the form of a flow chart in accordance with an illustrative embodiment. The process illustrated in Figure 6 can be implemented to manage the movement of the number of mobile platforms 102 in Figure 1.

The process may begin by generating a plurality of data streams 126 in which the plurality of data streams 126 includes the number of the first type of data streams 132 and the number of the second type of data streams 130 (step 600). The probability distribution 137 can then be applied to each of the number of the second type of data streams 130 to form the number of modified data streams 129 (step 602).

After that, the number of the first type of data streams 132 and the number of modified data streams 129 can be merged to generate a pose estimate 122 for the mobile platform 104 relative to the environment 100 around the mobile platform 104 with a desired level of precision (step 604), with the process ending afterwards. In step 604, merging may mean using Bayesian estimation techniques to generate a pose estimate 122.

Referring now to Figure 7, an illustration of a process for guiding a mobile robot within a manufacturing environment is shown in the form of a flow chart in accordance with an illustrative embodiment. The process illustrated in Figure 7 can be implemented to manage the movement of the mobile robot 106 within the manufacturing environment 101 in Figure 1. In particular, the process in Figure 7 can be used to reduce error when moving the robot 106 mobile along Route 117 in environment 100.

The process can begin by identifying the desired location 140 to which the mobile robot 106 should move (step 700). Next, a sensor system of the on-board data system 144 for the mobile robot 106 is used to search for the reference point 146 in the environment 100, while the mobile robot 106 is at a current location (step 702) . Next, the current reference point in environment 100 is observed a selected number of times using the sensor system of the onboard data system 144 with the mobile robot 106 at the current location (step 704). Next, the onboard data system 144 identifies an initial relative distance between the current location of the mobile robot 106 and the current reference point (step 706). At step 706, this identification can be an estimate.

Subsequently, the mobile robot 106 moves to a new location as far as possible towards the direction of the desired location 140 for the mobile robot 106 without losing the current reference point in the field of view of the sensor system of the data system 144. on board (operation 708). The current set point is re-observed a selected number of times using the on-board data system 144 sensor system, while the mobile robot 106 is at the new location (step 710). The on-board data system 144 identifies a new relative distance between the new location of the mobile robot 106 and the current reference point (step 712). In step 712, this identification can be an estimate.

The onboard data system 144 calculates the difference between the initial relative distance and the new relative distance (step 714). An estimate of the new location of the mobile robot 106 is then identified using the difference (step 716). It is then determined if the new location is at the desired location 140 within the selected tolerances (step 718). If the new location is at the desired location 140 within the selected tolerances, the process ends.

Otherwise, the onboard data system 144 searches for a new reference point 147, while at the new location (step 720). While at the new location, the onboard data system 144 then observes the new reference point 147 the selected number of times using the onboard data system 144 sensor system (step 722). The on-board data system 144 then identifies a relative distance between the new location of the mobile robot 106 and the new reference point 147 (step 722). In step 722, this identification can be an estimate. In this way, the new reference point 147 can be considered "correlated" with the reference point 146 at the new location. The process then re-identifies the new waypoint 147 as the current waypoint, the new location as the current location, and the relative distance as the initial relative distance (step 724), and the process returns to step 708 as described above.

Turning now to Figure 8, an illustration of a data processing system is shown in the form of a block diagram in accordance with an illustrative embodiment. The data processing system 800 can be used to implement the controller 114 in Figure 1. As shown, the data processing system 800 includes the communications frame 802, which provides communications between the processor unit 804, the devices Storage 806, communication unit 808, input / output unit 810, and display 812. In some cases, communication frame 802 may be implemented as a bus system.

The processor unit 804 is configured to execute instructions for the software to perform a number of operations. Processor unit 804 may comprise a number of processors, a multiprocessor core, and / or some other type of processor, depending on the implementation. In some cases, the processor unit 804 may take the form of a hardware unit, such as a circuit system, an application-specific integrated circuit (ASIC), a programmable logic device, or some other suitable type of hardware unit.

Instructions for the operating system, applications, and / or programs run by the processor unit 804 may be located on storage devices 806. Storage devices 806 may be in communication with processor unit 804 via communication frame 802. As used herein, a storage device, also known as a computer-readable storage device, is any piece of hardware capable of storing information temporarily and / or permanently. This information may include, but is not limited to, data, program code, and / or other information.

Memory 814 and persistent storage 816 are examples of storage devices 806. Memory 814 can take the form of, for example, random access memory or some type of volatile or non-volatile storage device. Persistent storage 816 can comprise any number of components or devices. For example, persistent storage 816 may comprise a hard disk, flash memory, rewritable optical disk, rewritable magnetic tape, or some combination of the above. The media used by persistent storage 816 may or may not be removable.

Communication unit 808 allows data processing system 800 to communicate with other data processing systems and / or devices. Communication unit 808 can provide communications using physical and / or wireless communication links.

The input / output unit 810 allows input to be received and output to be sent to other devices connected to the data processing system 800. For example, the input / output unit 810 may allow the input input is received via a keyboard, mouse, and / or some other type of input device. As another example, the input / output unit 810 may allow the output to be sent to a printer connected to the data processing system 800.

Display 812 is configured to display information to a user. The screen 812 may comprise, for example, without limitation, a monitor, a touch screen, a laser screen, a holographic screen, a virtual screen device, and / or some other type of screen device.

In this illustrative example, the processes of the different illustrative embodiments can be performed by processor unit 804 using computer-implemented instructions. These instructions may be referred to as program code, computer-usable program code, or computer-readable program code, and may be read and executed by one or more processors in processor unit 804.

In these examples, the program code 818 is located in functional form on a computer-readable medium 820, which is selectively removable, and can be loaded into or transferred to the data processing system 800 for execution by the data processing unit 804. processor. The program code 818 and the computer-readable media 820 together form the computer program product 822. In this illustrative example, the computer-readable media 820 may be computer-readable storage media 824 or computer-readable signal media 826.

Computer-readable storage medium 824 is a physical or tangible storage device used to store program code 818 in lieu of a medium that propagates or transmits program code 818. The computer-readable storage medium 824 may be, for example, an optical device or magnetic disk or a persistent storage device that is connected to the data processing system 800.

Alternatively, the program code 818 may be transferred to the data processing system 800 using computer-readable signal means 826. The computer-readable signal means 826 may be, for example, a propagated data signal containing the program code 818. This data signal can be an electromagnetic signal, an optical signal, and / or some other type of signal that can be transmitted over physical and / or wireless communication links.

The illustration of the data processing system 800 in Figure 8 is not intended to provide architectural limitations on the manner in which the illustrative embodiments may be implemented. The different illustrative embodiments may be implemented in a data processing system that includes components in addition to or in place of those illustrated for the data processing system 800. In addition, the components shown in Figure 8 may vary from the illustrative examples shown.

The flow diagrams and block diagrams in the different embodiments shown illustrate the architecture, functionality, and operation of some possible implementations of apparatus and methods in an illustrative embodiment. In this regard, each block in the flowcharts or block diagrams can represent a module, a segment, a function and / or a portion of an operation or stage.

In some alternative implementations of an illustrative embodiment, the function (s) indicated in the blocks may occur outside of the order indicated in the figures. For example, in some cases, two blocks displayed in succession may run substantially simultaneously, or the blocks may sometimes run in the reverse order, depending on the functionality involved. Additionally, other blocks can be added in addition to the blocks illustrated in a flow chart or block diagram.

Illustrative embodiments of the disclosure may be described in the context of aircraft manufacturing and servicing method 900 as shown in Figure 9 and aircraft 1000 as shown in Figure 10. Turning first to Figure 9, an illustration is shown. of an aircraft manufacturing and servicing method in block diagram form according to an illustrative embodiment. During pre-production, aircraft manufacturing and servicing method 900 may include specification and design 902 of aircraft 1000 in Figure 10 and material procurement 904.

During production, manufacturing 906 of components and sub-assemblies and system integration 908 of aircraft 1000 in Figure 10 take place. Thereafter, aircraft 1000 in Figure 10 can go through certification and 910 delivery with in order to be 912 commissioned. While 912 is in service by a customer, the aircraft 1000 in Figure 10 is scheduled for routine 914 maintenance and service, which may include modifications, reconfiguration, refurbishment and other maintenance or service.

Each of the aircraft manufacturing processes and service method 900 can be performed or carried out by a systems integrator, a third party, and / or an operator. In these examples, the operator can be a customer. For the purposes of this description, a systems integrator may include, without limitation, any number of aircraft manufacturers and major systems subcontractors; a third party may include, without limitation, any number of vendors, subcontractors and suppliers; and an operator can be an airline, a leasing company, a military entity, a service organization, etc.

Referring now to Figure 10, an illustration of an aircraft in the form of a block diagram is shown in which an illustrative embodiment can be implemented. In this example, aircraft 1000 is produced by aircraft manufacturing and service method 900 in Figure 9 and may include airframe 1002 with a plurality of systems 1004 and interior 1006. Examples of systems 1004 include one or more of the propulsion system 1008, the electrical system 1010, the hydraulic system 1012 and the environmental system 1014. Any number of other systems can be included. Although an aerospace example is shown, different illustrative embodiments can be applied to other industries, such as the automotive industry.

The apparatus and methods incorporated herein may be employed during at least one of the aircraft manufacturing stages and the service method 900 in Figure 9. In particular, a number of mobile platforms 102 may be used during any of the stages of aircraft manufacturing and the 900 method of service. For example, without limitation, the number of mobile platforms 102 may be used to perform operations during at least one of component and sub-assembly manufacturing 906, system integration 908, routine maintenance and service 914, or some other stage of maintenance. aircraft manufacturing and method 900 of service.

In an illustrative example, components or sub-assemblies produced in component and sub-assembly manufacturing 906 in Figure 9 may be fabricated or manufactured similarly to components or sub-assemblies produced at the time aircraft 1000 is in service 912 in Figure 9. As yet another example, one or more embodiments of the apparatus, embodiments of the method, or a combination thereof may be utilized during production steps, such as component and sub-assembly fabrication 906 and system integration 908 in Figure 9. One or more embodiments of the apparatus, embodiments of the method, or a combination thereof will be used while aircraft 1000 is in service 912 and / or during maintenance and service 914 in Figure 9. The use of a number of the different illustrative embodiments can substantially speed up assembly and / or reduce the cost of the aircraft 1000.

The description of the various illustrative embodiments has been presented for illustrative and descriptive purposes, and is not intended to be exhaustive or limited to the embodiments as disclosed. Various modifications and variations will be apparent to those skilled in the art. Furthermore, different illustrative embodiments can provide different characteristics compared to other desirable embodiments. The selected embodiment or embodiments are chosen and described to better explain the principles of the embodiments, the practical application, and to enable others skilled in the art to understand the disclosure of various embodiments with various modifications that are suitable for the particular use contemplated.

Claims (13)

1. An apparatus comprising: a plurality of data systems (128) configured to generate a plurality of data streams (126) in which the plurality of data streams includes a first type of data stream (132) and a second type of stream (130) data, wherein each data point in the first type of data stream includes an uncertainty measurement, and wherein each data point in the second type of data stream does not include an uncertainty measurement; a modifier (120) configured to apply a probability distribution (137) to the second type of data stream to form a modified data stream (135); and a pose estimator (118) located aboard a mobile platform (104) and configured to receive and merge the first type of data stream and the modified data stream to generate a pose estimate (122) with a desired level of accuracy for the mobile platform with respect to an environment (100) around the mobile platform; where the device is configured to: observing a current landmark in the environment a selected number of times using an on-board data system (134), while the mobile platform is at a current location; identifying an initial relative distance between the current location of the mobile platform and the current reference point; and moving the mobile platform from the current location to a new location as far as possible to a desired location without losing the current reference point within a field of view of an onboard data system sensor system. The apparatus of claim 1, wherein at least one of the number of the second type of data streams is received from an odometry system in the plurality of data systems. The apparatus of any one of claims 1 to 2, wherein the plurality of data systems includes a number of onboard data systems and a number of outboard data systems. The apparatus of any one of claims 1 to 3, wherein the pose estimation comprises a position and an orientation of the mobile platform relative to the environment. The apparatus of any one of claims 1 to 4, wherein the mobile platform is a mobile robot and the environment is a manufacturing environment. The apparatus of any one of claims 1 to 5, wherein the pose estimator is configured to merge the number of the first type of data streams and the number of modified data streams using a Bayesian estimation algorithm to generate the pose estimate. 7. The apparatus of any one of claims 1 to 6 further comprises: a controller configured to use pose estimation to guide the mobile platform along a path within the environment. 8. A method for guiding a mobile platform (104) within an environment (100), the method comprising: generating a first type of data stream (132) and a second type of data stream (130) using a plurality of data systems (128), wherein each data point in the first type of data stream includes an uncertainty measurement, and wherein each data point in the second type of data stream does not include an uncertainty measurement; applying a probability distribution (137) to the second type of data stream to form a modified data stream (135); merging the first type of data stream and the modified data stream to generate a pose estimate (122) with a desired level of precision for the mobile platform relative to the environment around the mobile platform; observing a current landmark in the environment a selected number of times using an on-board data system (134), while the mobile platform is at a current location; identifying an initial relative distance between the current location of the mobile platform and the current reference point; and moving the mobile platform from the current location to a new location as far as possible to a desired location without losing the current reference point within a field of view of an onboard data system sensor system. The method of claim 8 further comprises: guide the mobile platform along a path in the environment using the pose estimate generated for the mobile platform. The method of claim 8 or claim 9, wherein applying the probability distribution to each of the number of the second type of data streams to form the number of modified data streams comprises: applying an empirical covariance to each point of data in each of the numbers of the second type of data streams to form the number of modified data streams. The method of any one of claims 8 to 10, wherein merging the number of the first type of data streams and the number of modified data streams comprises: merging the number of the first type of data streams and the number of Modified data streams using a Bayesian estimation algorithm to generate the pose estimate with the desired level of precision. The method of any one of claims 8 to 11, wherein generating the number of the first type of data streams and the number of the second type of data streams comprises: generating the number of the second type of data streams using at least a color and depth odometry system, a wheel odometry system, a visual odometry system, a light detection and measurement system, or a locator and mapper. The method of any one of claims 8 to 12, further comprises: re-observing the current waypoint the selected number of times using the onboard data system; identifying a new relative distance between the new location of the mobile platform and the current reference point; calculate a difference between the initial relative distance and the new relative distance; and identify the new location of the mobile platform in the environment using the difference.